Job Description SummaryGE Vernova's Power Conversion & Storage business is at the forefront of the energy transition. We are seeking several highly skilled Senior AI Engineers to join our teams to design, develop, and deliver AI-driven solutions that improve efficiency and decision-making across our business.
In this role, you will collaborate closely with domain experts and cross-functional teams to apply artificial intelligence, generative AI, and machine learning to real-world industrial challenges, helping accelerate innovation, productivity, and operational excellence.
A defining part of the role is technical judgment; choosing the right tool for each problem: classical machine learning, deep learning, GenAI, or pure software development when needed.
Job DescriptionAI & ML Solution Development & Integration- Design, develop, and implement AI solutions, including generative AI, machine learning models, neural networks, and optimization algorithms, to improve business process efficiency and effectiveness.
- Select the appropriate modelling approach for each problem and articulate the trade-offs behind that choice.
- Own the machine learning lifecycle: dataset construction, metrics definition, acceptance criteria in accordance with the stakeholders needs, evaluation strategies.
- Translate business and operational needs into scalable AI-enabled tools, applications, and workflows.
- Support the deployment and integration of AI/ML models into existing business and technical systems, software environments, and products where applicable, including monitoring for drift, performance degradation, and running cost.
Technical Implementation & Architecture- Collaborate with domain experts to identify high-value use cases and define technical requirements for AI solutions.
- Define and document the solution architecture end to end: from data sources to the integration with the existing enterprise and technical IT landscape.
- Design cloud-ready and on-premises solutions aligned with company IT, cybersecurity and data-governance standards.
- Integrate AI/ML capabilities into hardware, software, and business process ecosystems in a way that supports reliability, usability, and maintainability.
- Contribute to the development of robust, production-ready AI solutions suitable for industrial environments.
Solution Delivery, Partner & Contractor Management- Write clear technical specifications, statements of work, and acceptance criteria for work delivered by external contractors, software vendors, or internal digital teams.
- Contribute to supplier, platform, and tool selection through structured technical evaluation, benchmarking, and proof-of-concept comparison.
- Steer and review the work of internal & external partners: technical follow-up, design reviews, code and model reviews, quality gates, and acceptance testing; remaining the technical owner and guardian of the delivered solution.
- Ensure solutions remain maintainable after handover through documentation, knowledge transfer, and clearly assigned ownership, so that delivered tools do not become orphaned.
Data Strategy & Analytics- Lead or support the collection, processing, structuring, and analysis of large-scale operational and business data.
- Assess data readiness ahead of any development (availability, quality, labelling needs, access rights, confidentiality) and define strategies to close the gaps.
- Identify patterns, trends, and performance improvement opportunities using advanced analytics and AI methods.
- Develop data-driven solutions such as predictive maintenance, anomaly detection, quality and performance prediction, forecasting, cost analysis, document and requirement analysis, and knowledge support tools.
Cross-Functional Collaboration- Work closely with technical, operational, business and IT teams to ensure AI solutions meet business and industry requirements for safety, reliability, performance, and scalability.
- Communicate technical concepts clearly to both technical and non-technical stakeholders.
- Help align AI initiatives with business priorities, operational goals, and constraints.
- Support end-user adoption: training, onboarding, feedback loops and measurement of the benefits realized once the solution is live.
Continuous Innovation- Evaluate emerging technologies such as edge AI, synthetic data, reinforcement learning, and large language models for industrial and business applicability.
- Stay current with developments in AI, machine learning, and digital tools, and recommend practical adoption opportunities.
- Maintain an active technology watch on the AI tooling landscape and filter it, distinguishing capability gains from hype before proposing adoption.
- Contribute to building an innovation-oriented culture through knowledge sharing, experimentation, and continuous improvement.
Your ProfileEducation- Master's degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field.
Experience- Overall 8+ Years of experience.
- Several years (2 to 4) of professional experience in artificial intelligence, machine learning, data science, software engineering, or a comparable technical role.
- Experience developing and deploying AI/ML solutions in industrial, technical, or other complex operational environments is preferred.
Technical Expertise- Strong programming skills in Python, C++, or similar languages, and proficiency with modern development environments such as VS Code.
- Hands-on experience with machine learning and deep learning frameworks such as TensorFlow and/or PyTorch, as well as classical ML tooling (e.g. scikit-learn, gradient boosting methods).
- Experience with time-series analysis, optimization methods, and data-driven model development.
- Practical experience with GenAI and their surrounding stacks (RAG, vector databases, A2A)
- Experience handling unstructured data; technical documents, specifications, reports; alongside structured and tabular data.
- Solid grounding in cloud services and architecture (Azure, AWS)
- Working knowledge of data engineering fundamentals: SQL, data pipelines, and structured/unstructured data handling.
- Familiarity with MLOps practices, model deployment, and integration into production environments is an advantage.
Domain Knowledge- Sound knowledge of artificial intelligence, combined with a strong interest in emerging technologies and digital trends.
- Understanding of industrial processes, electrification, power systems, or related technical domains, or business processes, is an advantage.
- Awareness of the regulatory and governance context around AI (e.g. EU AI Act, GDPR) is a plus.
- Experience in innovation management and/or patent-related work is a plus.
Personal Attributes- Proven ability to translate complex business and technical challenges into practical, scalable AI-driven solutions.
- Strong analytical and strategic thinking, with a high degree of self-motivation and a structured, goal-oriented working style.
- Strong documentation skills and attention to detail.
- Collaborative mindset with the ability to work effectively across functions and disciplines.
- Excellent written and verbal communication skills in English.
Additional InformationRelocation Assistance Provided: Yes
